With the upgrading of consumption driving the transformation of the home furnishing industry towards personalized customization, panel furniture enterprises are confronted with a core contradiction between large-scale production and individualized demands: The traditional production management model is unable to cope with the chaos in production scheduling, resource waste, and low collaborative efficiency caused by small-batch and multi-variety orders. This paper proposes an intelligent production scheduling system that integrates Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), and Warehouse Management System (WMS), and elaborates on its data processing methods and specific application processes in each production stage. Compared with the traditional model, it effectively overcomes limitations such as coarse-grained planning, delayed execution, and information islands in middle-level systems, achieving deep collaboration between planning, workshop execution, and warehouse logistics. Empirical studies show that this system not only can effectively reduce the production costs of customized panel furniture manufacturers, enhance their market competitiveness, but also provides a digital transformation framework for the entire customized panel furniture manufacturing industry, with significant theoretical and practical value.

To enhance consumers’ preference for the appearance of cork flooring, this work employed the semantic differential method to explore the relationship between consumers’ visual perceptions and their preferences. First, a collection of cork flooring product images was assembled, and a lexicon of perceptual vocabulary describing the visual characteristics of cork flooring was developed. Subsequently, a survey based on the semantic differential method and preference questionnaires was completed. A regression model was established to analyze the relationship between the scores of perceptual vocabulary and consumer preferences. The results indicated that the perceptual terms “light” and “warm” had a significant positive impact on consumer preferences. Furthermore, the study explored the relationship between the granularity, grain arrangement, and color of cork flooring samples and the perceptual vocabulary, revealing distribution patterns of the samples in terms of “light” and “warm” characteristics. The findings suggest that increasing the saturation and brightness of cork flooring surface colors, reducing granularity, and enhancing the rhythmic arrangement of patterns can improve consumers’ preference for the appearance of cork flooring.

The Atkins model has been widely adopted for determining mechanical properties of wood, such as fracture toughness and shear yield stress, which are typically normalised by global density for cutting force and power predictions. This study explores the feasibility of determining these mechanical properties for knotty and clear Scots pine (Pinus sylvestris L.) using local densities revealed by X-ray computed tomography scanning. Six wood workpieces, three from Poland and three from Sweden, were scanned and subsequently cut on a custom single-tooth quasi-linear cutting machine. Cutting forces for both clear and knotty regions were recorded and normalised by local densities. Results indicate that clear Polish pine exhibits higher local-density-normalised fracture toughness and shear yield stress than Swedish pine, suggesting that wood origin influences mechanical properties beyond mere density differences. Knots display significantly lower local-density-normalised shear yield stress compared to clear wood, despite their higher density. The large variation in normalised fracture toughness observed in knots is attributed to differences in cutting direction relative to knot orientation. The study highlights the effectiveness of computed tomography scanning to provide detailed insights into wood density and structure, enabling more accurate normalization of cutting forces and enhancing the understanding of wood machinability across different origins and structural characteristics.

Wood–plastic composites (WPCs) are important materials used in interior architectural decorations and landscape construction products. Enhancing the cutting performance of WPCs is of great significance for improving both production efficiency and product quality in factories. This study aims to elucidate the impact of fluorine nano-coating technology on the cutting performance of cemented carbide tools during the milling of WPCs. The main results are given as follows. The cutting force and surface roughness showed similar trends with the varied parameters; both increased with increasing cutting depth and decreased with increasing cutting speed. The fluorine nano-coating technology exerts a positive influence on the cutting performance in terms of lower cutting forces and surface roughness. Meanwhile, based on the analysis of variance results, the experimental factors of cutting speed, depth, and surface treatment had a significant contribution to both cutting force and surface roughness, and cutting depth had the greatest impact on cutting force and surface roughness, followed by cutting speed and tool surface treatment. In general, the cutting performance of WPCs can be improved by higher cutting speed and lower depth, with the tool surface treated with fluorine nano-coating.

Heat-treated pine wood is commonly utilized in the furniture and construction sectors, with milling being a key technique to enhance the surface quality of these products. To investigate the milling surface damage mechanism of heat-treated wood, a milling test of pine wood was conducted after different heat treatments, and the effects of heat treatment temperature and cutting parameters (cutting depth and feed per tooth) on cutting force and surface roughness were analyzed. The experimental and analytical results of these cutting tests indicate that higher heat treatment temperature resulted in reduced wood strength, leading to a reduction in cutting force as the heat treatment temperature increased. Additionally, the brittleness of wood increased with increasing heat-treatment temperature, which caused more burrs and wood tissue fragments to appear on the machined surface, resulting in increased surface roughness. Increasing cutting depth from 0.1 mm to 0.5 mm raises cutting force and surface roughness for untreated and heat-treated wood. For depth, force increases by 35.5% to 15.32% and roughness by 75.8% to 84.7%. For feed speed from 0.2 mm/Z to 0.6 mm/Z, force increases by 37.55% to 34.56% and roughness by 58.38% to 91.4%. This study investigates the milling surface damage mechanisms of heat-treated wood, filling the gap in the literature that has focused on the effects of cutting parameters on surface roughness without examining surface damage mechanisms, providing a theoretical basis for optimizing the processing technology of heat-treated wood.

In industrial bamboo machining, the suboptimal selection of cutting parameters leads to elevated cutting power and increased surface roughness. To enhance the machinability of bamboo, a multi-objective optimization of cutting parameters was conducted using orthogonal experimental methods, with special focus on the influences of fiber direction, feed per tooth, and cutting speed on cutting power and surface roughness. The main findings of this study are summarized as follows: feed per tooth exhibited the greatest effect on cutting power, followed by cutting speed and fiber direction. In contrast, fiber direction exerted the most substantial influence on surface roughness, with feed per tooth and cutting speed ranking second and third, respectively. Furthermore, the optimal milling parameters for minimizing both cutting power and surface roughness were identified as a fiber direction of 0°, a feed per tooth of 0.2 mm/z, and a cutting speed of 400 m/min. Therefore, the obtained optimal parameters are recommended for industrial bamboo machining to achieve reduced cutting power and improved surface quality.

In wood–plastic composites (WPCs) milling, achieving optimal material removal rates and surface roughness levels are critical objectives. In this study, WPCs milling experiments were conducted, and a back propagation (BP) neural network was applied to develop a predictive model for surface roughness. A geometric method was used to derive the calculation formula for the material removal rate. Subsequently, a multi-objective approach was adopted to determine the optimal combination of control factors, including spindle speed n, feed rate U, milling depth h, for WPCs milling. The findings indicate that an increase in spindle speed reduced surface roughness, whereas higher feed speed and milling depth resulted in increased surface roughness. Variance analysis revealed that milling depth had the greatest impact on surface roughness, contributing 34.66%, followed by feed speed at 30.77% contribution and spindle speed at 30.55% contribution. A BP prediction model for surface roughness was established with high accuracy, exhibiting a maximum error of 4.89%. Furthermore, a multi-objective particle swarm optimization algorithm was applied to optimize the objectives of minimizing surface roughness and maximizing material removal rate. Based on the obtained Pareto front, the milling parameter combination of n = 12,000 r/min, U = 3.23 m/min, and h = 0.4 mm is recommended for roughing. For semi-finishing, the optimal parameters are n = 12,000 r/min, U = 4.76 m/min, and h = 0.4 mm. For finishing, the suitable combination is n = 12,000 r/min, U = 6 m/min, and h = 0.72 mm. Experimental verification demonstrated a maximum predictive error of 16.87%, confirming the feasibility of the multi-objective optimization approach.

Sustainable development is imperative amid increasing energy crises and fossil fuel pollution. Crop straw, a common agricultural waste, can be converted into high-value biochar through thermal carbonization. In this study, corn, cotton, rice, and sorghum straws were pyrolyzed at 300–500 °C, and the resulting biochars were characterized for morphology, elemental composition (C/H/N), pore distribution, and surface chemistry. Thermogravimetry indicated that 450 °C was optimal for retaining maximal residual mass. Higher temperatures increased aromatization and surface smoothness. Sorghum-derived biochar exhibited the largest specific surface area (442.71 m²/g) and the highest N/C ratio (0.031), whereas rice biochar showed the lowest N/C ratio (0.019). All crop straw biochar pore sizes were predominantly 2–10 nm (average size ≈ 4 nm). Germination assays with rapeseed revealed significant enhancement (86.11%–91.67% germination) across all biochar types relative to the control. Microbial cultivation confirmed that biochar fosters a beneficial microenvironment. These findings demonstrate the agronomic potential of straw biochar and provide a reference for optimizing feedstock selection and pyrolysis parameters for plant growth and soil amendment.

Scots pine (Pinus sylvestris L.) wood is distinguished by its outstanding mechanical properties compared to other medium-density woods, and it is emerging as a material of choice in the woodworking community for its potential in modern construction practices. However, the milling processes for this valuable resource have yet to be optimised. This study examined the effectiveness of end milling in Scots pine, focusing on three key operational parameters: depth of cut, spindle speed, and cutting speed. The objective of this study was to systematically determine how these parameters influence the crucial milling performance quality metrics of cutting force and surface roughness, both independently and in combination. To extract the cutting parameters with the most significant impact on cutting force and surface quality, unsupervised machine learning tools for classification and prediction were applied, specifically, principal component analysis and projections to latent structures. This multivariate approach revealed that cutting force correlates positively with both cutting speed and depth of cut. Meanwhile, surface quality is mainly affected by depth of cut in a nonlinear manner. This study applied methods to assess the impacts of variable adjustments on milling outcomes resulting in guidelines for the woodworking industry to improve efficiency and product quality. 

Excessive cutting temperature adversely affects the machining quality of wood–plastic composites (WPCs). Cryogenic CO2 cutting technology offers a potential solution to this issue. This study aimed to investigate the influence mechanism of cryogenic CO2 cutting on WPC surface quality. Comparative experiments under dry and cryogenic CO2 cutting conditions were conducted, focusing on the effects of cutting speed and cutting depth on cutting temperature, cutting force, and surface roughness. Results indicate that cryogenic CO2 cutting effectively reduces the cutting temperature by 28% and 28% reduction at low cutting speeds and shallow cutting depths, respectively. This is attributed to enhanced heat transfer and inhibition of built-up edge formation under cryogenic conditions. At a cutting depth of 0.1 mm, the cutting force of cryogenic CO2 cutting decreases because of BUE suppression. But at greater cutting depths, the cutting force in cryogenic conditions is higher because the larger hardness in cryogenic condition increases the cutting resistance. Finally, cryogenic CO2 changes the dominant material removal mechanism from ductile removal to brittle removal. This helps reduce burr generation and improves surface quality. Therefore, cryogenic CO2 cutting technology can be used to improve the processing quality of WPC finishing.